# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. import logging from typing import AsyncGenerator from huggingface_hub import AsyncInferenceClient, HfApi from llama_models.llama3.api.chat_format import ChatFormat from llama_models.llama3.api.datatypes import StopReason from llama_models.llama3.api.tokenizer import Tokenizer from llama_stack.apis.inference import * # noqa: F403 from llama_stack.providers.utils.inference.augment_messages import ( augment_messages_for_tools, ) from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig logger = logging.getLogger(__name__) class _HfAdapter(Inference): client: AsyncInferenceClient max_tokens: int model_id: str def __init__(self) -> None: self.tokenizer = Tokenizer.get_instance() self.formatter = ChatFormat(self.tokenizer) async def shutdown(self) -> None: pass async def completion( self, model: str, content: InterleavedTextMedia, sampling_params: Optional[SamplingParams] = SamplingParams(), stream: Optional[bool] = False, logprobs: Optional[LogProbConfig] = None, ) -> AsyncGenerator: raise NotImplementedError() def get_chat_options(self, request: ChatCompletionRequest) -> dict: options = {} if request.sampling_params is not None: for attr in {"temperature", "top_p", "top_k", "max_tokens"}: if getattr(request.sampling_params, attr): options[attr] = getattr(request.sampling_params, attr) return options async def chat_completion( self, model: str, messages: List[Message], sampling_params: Optional[SamplingParams] = SamplingParams(), tools: Optional[List[ToolDefinition]] = None, tool_choice: Optional[ToolChoice] = ToolChoice.auto, tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json, stream: Optional[bool] = False, logprobs: Optional[LogProbConfig] = None, ) -> AsyncGenerator: request = ChatCompletionRequest( model=model, messages=messages, sampling_params=sampling_params, tools=tools or [], tool_choice=tool_choice, tool_prompt_format=tool_prompt_format, stream=stream, logprobs=logprobs, ) messages = augment_messages_for_tools(request) model_input = self.formatter.encode_dialog_prompt(messages) prompt = self.tokenizer.decode(model_input.tokens) input_tokens = len(model_input.tokens) max_new_tokens = min( request.sampling_params.max_tokens or (self.max_tokens - input_tokens), self.max_tokens - input_tokens - 1, ) print(f"Calculated max_new_tokens: {max_new_tokens}") options = self.get_chat_options(request) if not request.stream: response = await self.client.text_generation( prompt=prompt, stream=False, details=True, max_new_tokens=max_new_tokens, stop_sequences=["<|eom_id|>", "<|eot_id|>"], **options, ) stop_reason = None if response.details.finish_reason: if response.details.finish_reason in ["stop", "eos_token"]: stop_reason = StopReason.end_of_turn elif response.details.finish_reason == "length": stop_reason = StopReason.out_of_tokens completion_message = self.formatter.decode_assistant_message_from_content( response.generated_text, stop_reason, ) yield ChatCompletionResponse( completion_message=completion_message, logprobs=None, ) else: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.start, delta="", ) ) buffer = "" ipython = False stop_reason = None tokens = [] async for response in await self.client.text_generation( prompt=prompt, stream=True, details=True, max_new_tokens=max_new_tokens, stop_sequences=["<|eom_id|>", "<|eot_id|>"], **options, ): token_result = response.token buffer += token_result.text tokens.append(token_result.id) if not ipython and buffer.startswith("<|python_tag|>"): ipython = True yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( content="", parse_status=ToolCallParseStatus.started, ), ) ) buffer = buffer[len("<|python_tag|>") :] continue if token_result.text == "<|eot_id|>": stop_reason = StopReason.end_of_turn text = "" elif token_result.text == "<|eom_id|>": stop_reason = StopReason.end_of_message text = "" else: text = token_result.text if ipython: delta = ToolCallDelta( content=text, parse_status=ToolCallParseStatus.in_progress, ) else: delta = text if stop_reason is None: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=delta, stop_reason=stop_reason, ) ) if stop_reason is None: stop_reason = StopReason.out_of_tokens # parse tool calls and report errors message = self.formatter.decode_assistant_message(tokens, stop_reason) parsed_tool_calls = len(message.tool_calls) > 0 if ipython and not parsed_tool_calls: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( content="", parse_status=ToolCallParseStatus.failure, ), stop_reason=stop_reason, ) ) for tool_call in message.tool_calls: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( content=tool_call, parse_status=ToolCallParseStatus.success, ), stop_reason=stop_reason, ) ) yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.complete, delta="", stop_reason=stop_reason, ) ) class TGIAdapter(_HfAdapter): async def initialize(self, config: TGIImplConfig) -> None: self.client = AsyncInferenceClient(model=config.url, token=config.api_token) endpoint_info = await self.client.get_endpoint_info() self.max_tokens = endpoint_info["max_total_tokens"] self.model_id = endpoint_info["model_id"] class InferenceAPIAdapter(_HfAdapter): async def initialize(self, config: InferenceAPIImplConfig) -> None: self.client = AsyncInferenceClient( model=config.model_id, token=config.api_token ) endpoint_info = await self.client.get_endpoint_info() self.max_tokens = endpoint_info["max_total_tokens"] self.model_id = endpoint_info["model_id"] class InferenceEndpointAdapter(_HfAdapter): async def initialize(self, config: InferenceEndpointImplConfig) -> None: # Get the inference endpoint details api = HfApi(token=config.api_token) endpoint = api.get_inference_endpoint(config.endpoint_name) # Wait for the endpoint to be ready (if not already) endpoint.wait(timeout=60) # Initialize the adapter self.client = endpoint.async_client self.model_id = endpoint.repository self.max_tokens = int( endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"] )